Our first theorem is an upper bound on the excess risk of any constrained least squares estimator
\(\widehat{\omega }_{b}^{\operatorname{ERM}}\) . The proof is deferred to Section REF . We remark that the below upper bound is non-asymptotic, in contrast to the agnostic upper bound proved in [1]}. Also, we make no restrictions on the sample size \(n\)  and the uniqueness of least squares, as opposed to results that hold for the unconstrained least squares estimator (e.g., [2]}).
